Genome-Wide association study identifies new elements on the genetic basis of quality-related traits in wheat across multiple environments

doi:10.21203/rs.3.rs-99775/v1

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Genome-Wide association study identifies new elements on the genetic basis of quality-related traits in wheat across multiple environments

https://doi.org/10.21203/rs.3.rs-99775/v1

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Backgrounds: Grain protein concentration (GPC), grain starch concentration (GSC), and wet gluten concentration (WGC) are complex traits that determine nutrient concentration, end-use quality, and yield in wheat. To identify the elite and stable loci or genomic regions conferring high GPC, GSC, and WGC, a genome-wide association study (GWAS) based on a mixed linear model (MLM) was performed using 55K single nucleotide polymorphism (SNP) array in a panel of 236 wheat accessions, including 160 commercial varieties and 76 landraces, derived from Sichuan Province, China. The panel was evaluated for GPC, GSC, and WGC at four different fields.

Results: Phenotypic analysis showed variation in GPC, GSC, and WGC among the different genotypes and environments. GWAS identified 12 quantitative trait loci (QTL) (-log10(P) > 2.5) associated with these three quality traits in at least two environments and located on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 5D, and 7D; the phenotypic variation explained (PVE) by these QTL ranged from 4.2% to 10.7%. Among these, three, seven, and two QTL are associated with GPC, GSC, and WGC, respectively; five QTL (QGsc.sicau-1BL, QGsc.sicau-1DS, QGsc.sicau-2DL.1, QGsc.sicau-2DL.2, QWgc.sicau-5DL) were defined potentially novel Compared with the previously reported QTLs/genes by linkage or association mapping, 5 QTLs (QGsc.sicau-1BL, QGsc.sicau-1DS, QGsc.sicau-2DL.1, QGsc.sicau-2DL.2, QWgc.sicau-5DL) were potentially novel. Furthermore, 21 presumptive candidate genes, which are involved in the metabolism or transportation of all kinds of carbohydrates, photosynthesis, programmed cell death, the balance of abscisic acid and ethylene, within these potentially novel genomic regions were predicted.

Conclusions: This study provided new genetic resources and valuable genetic information of nutritional quality to broaden the genetic background and laid the molecular foundation for marker-assisted selection in wheat quality breeding.

Epigenetics & Genomics

Commercial variety

landrace wheat

grain protein concentration (GPC)

grain starch concentration (GSC)

wet gluten concentration (WGC)

55K SNP

genome-wide association study

Wheat (Triticum aestivum L.) is a culturally and historically important staple food consumed by one-third of the world’s population daily dietary intake of protein, it the most important source of protein, starch and energy globally. The grain protein concentration is also one of the major pricing factors for wheat trading. Grain protein concentration (GPC) and grain starch concentration (GSC) are the major traits that determine yield, nutritional and processing quality. GPC has strong relationship with wet gluten concentration (WGC) and determine the end-use. With the increase of atmospheric carbon dioxide concentration, a decline in the grain protein concentration has been observed [1] and poses a threat to human nutrition; plus increasing in population enlarge the demand of wheat, there is a need to increase nutritional quality via breeding, so the focus of this study is therefore primarily centre on the important nutritional traits GPC, GSC and WGC.

In wheat, GPC, GSC, and WGC are complex quality traits strongly influenced by genetic and environmental factors and management procedures. Several studies have reported the significant effects of environmental factors on GPC and WGC [2–4]. GPC, which affects the baking properties, varies among the different wheat cultivars (10–20%) [5]. Generally, wheat can be separated into bread wheat, noodle wheat and cookies wheat according to the end use based on GPC to some extent. The results showed that (1) significant influence of environment on GPC, (2) negative correlation between GPC and grain yield, and (3) presence of many loci with small genetic effects on GPC and low heritability of the trait[2, 6, 7]. Therefore, independent and stable quantitative trait loci (QTL) or genes with significant effects on GPC are better for breeding. Starch, composed of amylopectin and amylose [8], is the major component in the wheat endosperm that accounts for almost three-quarters of grain composition. The GSC influences grain processing and end-use quality of traditional flour products, such as noodles and steamed bread [9, 10]. It is positively correlated with grain yield in wheat [11–13]. WGC plays a pivotal role in determining the bread-baking quality and pasta-making technological properties. It is positively correlated with GPC and negatively correlated with GSC [11, 14, 15]. Grains with high WGC levels are suitable for bread-type products, and those with low WGC levels are suitable for cake-type products [16].

Recent molecular technologies have helped identify many QTL associated with quality traits. More than 500 QTL spreading over 21 chromosomes have been associated with GPC through linkage mapping based on biparental populations [4, 13, 16, 17–29]. Gpc-B1 is the most important gene that has resulted in maximum increase in GPC and has been widely used in many cultivars [30]. Approximately 250 QTL spread over 21 chromosomes were identified for GSC [13, 16, 29, 31–40]. This includes starch synthesis-related enzymes such as starch synthases, soluble starch synthase, starch branching enzyme, starch-debranching enzyme, and granule-bound starch synthase. Additionally, 130 QTL distributed over 21 chromosomes were identified for WGC [13, 16, 29, 38, 41–51]. However, studies on QTL associated with GSC and WGC are less.

GPC, GSC, and WGC are complex traits regulated by several loci with small genetic effects, genome-wide association studies (GWAS) can identify associations between phenotypic variation and nucleotide polymorphisms using a diverse population panel to elucidate the genetic effects on specific traits [52]. Large numbers of molecular markers have been developed that facilitate the progress of more efficient mapping techniques [53] and detect numerous natural allelic variations and historical chromosomal recombination events occur over multiple generations of natural populations. Recently complex agronomic traits and potential causal genes was detected by GWAS [54]. In this study, GWAS is used to detect QTL associated with variation in GPC, GSC, and WGC. We aim to identify more stable QTL associated with quality traits and broaden the genetic basis of wheat varieties. In this study, we analyze the three nutritional quality traits (GPC, GSC, and WGC) of 236 Sichuan wheat accessions with the following objectives: (1) evaluate GPC, GSC, and WGC of Sichuan wheat accessions in multiple environments; (2) select the elite germplasms for wheat quality breeding; and (3) identify the novel QTL of these three quality traits using genome-wide association study.

Evaluation of GPC, GSC, and WGC

Samples were collected from fields under four different environments (CZ17, CZ18, CZ19, and MY17) and the quality traits GPC, GSC, and WGC were measured. Pearson’s correlation analysis showed significant positive correlations in GPC, GSC, and WGC among all test environments (Supplementary Table 2). The BLUP values were computed to eliminate the impact of environmental factors and do the further analysis as follows. The statistical analysis revealed that the number of accessions for quality traits all followed the normal distribution (Fig. 1). GPC ranged from 12.02% to 14.13% (mean, 12.90%); GSC ranged from 51.24% to 59.89% (mean, 55.87%); and WGC from 22.08% to 26.67% (mean, 24.10%) (Table 1). ANOVA performed by MLM to determine the effects of environment and genotype revealed highly significant variation (P < 0.001) among the different environments for each quality trait; variation among the genotypes were significant at the level of P < 0.001. The genotype × environment interaction was significant for GPC and WGC. The broad-sense heritability (H²) of GPC, GSC, and WGC was 0.644, 0.841, and 0.656, respectively (Table 1).

Except for environment, stripe rust is another important influence on quality traits as it's a major epidemic disease for wheat in Sichuan province. The correlation analysis between IT and the quality traits revealed a significant negative correlation with GPC and WGC and a significant positive correlation with GSC. Among the three quality traits, GPC negatively correlated with GSC but positively correlated with WGC. Meanwhile, WGC and GSC showed a significant negative correlation (Table 2).

Differences in GPC, GSC, and WGC between landraces and cultivars

The t-test was performed to compare the significant differences (p < 0.05) of quality traits between all landraces and cultivars. Landraces had significantly higher GPC (p < 0.05) and WGC (p < 0.01), while the cultivars had significantly higher GSC (p < 0.01) (Table 3). Similarly, the analysis of Shannon-Weaver diversity index (H’) showed that the landraces had higher phenotypic diversity in GPC and WGC, while the cultivars had higher phenotypic diversity in GSC (Table 3).

Analysis of SNP markers

A total of 44,326 effective SNP markers were selected for further analysis from the accessions genotyped using 55K SNP array (missing values ≤ 10% and MAF ≥ 5%). Of these, 16,330, 16,831, and 11,165 SNP markers were mapped on subgenome A, B, and D, respectively, and covered a map length of 4930 million base pairs (Mb), 5176 Mb, and 3946 Mb, respectively. Every chromosome contained 2111 markers each, and the average distance between the markers was 0.32 Mb (average marker density was 3 markers per Mb). The chromosome 6A had the highest marker density (4.0 markers per Mb), while chromosome 4D had the lowest marker density (1.6 markers per Mb). The analysis of PIC showed that chromosome 5B had the highest PIC value (0.330), and 4A had the lowest (0.247) (Table 4).

Population structure and linkage disequilibrium (LD) decay

The population structure (Q-matrix) analysis using 44,326 SNP markers based on the Bayesian model showed the optimal ΔK as 2. The 236 accessions were classified into two subpopulations: subpopulation 1 (SP1) with 74 landraces and 1 cultivar (Xifu14) and subpopulation 2 (SP2) with 159 cultivars and 2 landraces (Kaixianluohanmai and Yupi) based on the three traits. Furthermore, we constructed the NJ phylogenetic tree based on shared allele distance. Obvious genetic difference was observed when the tree interval was 0.25. The accessions were divided into two clusters, cluster 1 and cluster 2. A total of 74 landraces and 2 cultivars (Xifu14 and Yumai1) belonged to cluster 1, and 158 cultivars, and 2 landraces (Kaixianluohanmai and Yupi) belonged to cluster 2 (Fig. 2).

LD across all chromosomes was estimated using r² between the significant pairs of intra-chromosomal SNP markers with physical distance. A total of 1,426,789 significant (pDiseq < 0.001 and r²> 0.1) pairwise SNP markers were identified. The average LD decay distance for the whole genome was around 1.922 Mb based on the best fitting curve (Fig. 3).

Genome-wide association study (GWAS)

GWAS was used to analyze the 44,326 SNP markers and the association with the quality traits in all test environments on the MLM model using Q and K as covariates. The loci with high confidences of association in the four environments were displayed as Manhattan plots with P values across the 21 wheat chromosomes (Fig 4). A total of 15 SNP markers were significantly associated with GPC, GSC, and WGC. Based on the LD decay distance (1.922 Mb), 12 QTL were identified located on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 5D, and 7D. The phenotypic variation explained (PVE) by the markers ranged from 4.2% to 10.75%. Of them, three QTL were significantly associated with GPC, seven with GSC, and two with WGC. Totally, five QTL were defined potentially novel through the physical mapping of the reported QTL/genes based on reference RefSeq v1.0 (Table 5).

Candidate gene prediction

A total of 243 genes (high confidence) were selected from the region of the five potentially novel QTL. Of them, 21 candidate genes were predicted to be involved in carbohydrate metabolism or transportation, photosynthesis, programmed cell death, and abscisic acid-ethylene balance that affected quality traits (Supplementary Table 3).

Impact of environment on GPC, GSC, and WGC

We analyzed GPC, GSC, and WGC of 236 wheat accessions including 76 landraces and 160 cultivars under four different field trials in Sichuan Province. We found the quality traits were heavily influenced by environments. The relatively smaller correlation coefficients and lower broad-sense variability (h²) among the different fields revealed the significant influence of environment on the quality traits (Supplementary Table 2; Table 2). Furthermore, ANOVA intuitively proved the significant differences in GPC, GSC, and WGC among the environments. These findings are consistent with many previous reports in wheat at different environment [4,13,55,56]. Meanwhile, H²of GSC was more than that of GPC and WGC. H² is an important genetic parameter used for phenotypic prediction that indicates all genetic contributions to phenotypic variance. High H² combined with relatively higher correlation coefficients among the four environments indicated a major impact of genotypes on GSC, and the possibility to improve traits via selective breeding. Earlier, Denčić [57] showed low h² of GPC and WGC in wheat. All these findings together indicate difficulty to improve GPC and WGC by traditional breeding; such traits should be selected at a later generation. So, traditional breeding combining with the maker-assisted selection is a good choice to improve the breeding efficiency and shorten the breeding period.

Impact of stripe rust on GPC, GSC, and WGC

Correlation coefficient analysis in this study showed that IT was negatively correlated with GPC and WGC but positively correlated with GSC (Table 1). The impact of environmental factors (including stripe rust) on GPC and WGC was more than that on GSC. And the relative higher h2 (0.841) indicated the GSC was stable and not easily influenced by the environment (Table 2). We considered that the decrease in GPC and WGC was more than that in GSC. Meanwhile, the present study and several previous reports [11,14,15] demonstrated a positive correlation between GPC and WGC but a negative correlation between GSC and GPC and between GSC and WGC. So, the analysis of the three quality traits and IT under certain conditions revealed a relative positive correlation between IT and GSC. If we compare the quality traits under control and treatment settings, there must be a negative correlation relationship between IT and the three quality traits.

Differences in quality traits between landraces and cultivars

In the present study, obvious differences were found between landraces and cultivars. Bayesian classification based on Q-matrix and neighbor-joining (NJ) phylogeny based on shared allele distance clearly grouped the accessions into landraces and cultivars. The phenotypic diversity analysis showed better performance of landraces with respect to GPC and WGC and that of cultivars with respect to WGC (Table 3). In addition, the genetic differences between landraces and cultivars were identified using SNP markers indicate that the use of landraces in breeding can broaden the genetic background and improve the phenotypic diversity.

QTL associated with the quality traits

In this study, 236 accessions were genotyped with 55K SNP array, and a total of 44,326 effective SNP markers were analyzed in this study. GWAS for the quality traits on these 236 accessions identified 12 QTL associated with GPC, GSC, and WGC. These QTL located on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 5D, and 7D were named as follows: QGpc.sicau-1BS, QGpc.sicau-2AS, QGpc.sicau-2BS, QGsc.sicau-1BL, QGsc.sicau-1DS, QGsc.sicau-2DL.1, QGsc.sicau-2DL.2, QGsc.sicau-3BS, QGsc.sicau-3DS, QGsc.sicau-5DS, QWgc.sicau-5DL, and QWgc.sicau-7DL.

Three QTL, QGpc.sicau-1BS, QGpc.sicau-2AS, and QGpc.sicau-2BS, were associated with GPC. QGpc.sicau-1BS was at around 51.99 Mb of the short arm on chromosome 1B with PVE that ranged from 5.7% to 6.2%, it was covered by QGPC.ndsu.1B [58]. QGpc.sicau-2AS was located at the distal region of the short arm of chromosome 2A. This QTL was similar to MQTL2A2 linked to wPt-4197 and wPt-5245 in wheat [59]. QTKW.sicau-2AS.1[60] associated with thousand-kernel weight was located on the same block as QGpc.sicau-2AS. QGpc.sicau-2BS, mapped on the short arm of chromosome 2B at about 25.42 Mb, was also associated with GPC. This QTL was located in the same region as QGpc.crc-2B flanked by the SSR markers Xgwm210 and Xwmc25[30]. QSlC.sicau-2BS [60] associated with spikelet compactness was located in the same region as QGpc.sicau-2BS. Thousand-kernel weight and spikelet compactness are important factors that determine grain yield in wheat. Moreover, several reports have demonstrated a close association between GPC and grain yield [4]. Thus, in the present study, QGpc.sicau-2BS identified was found associated with both GPC and thousand-kernel weight/spikelet compactness.

Furthermore, seven QTL associated with GSC were identified in this study. QGsc.sicau-3BS was associated with marker AX-110012661, located on the short arm of chromosome 3B, which was covered by QTsc-3B.12 linked to SSR marker Xwmc612 and Xbarc068[36]. QGsc.sicau-3DS was mapped on the short arm of chromosome 3D at around 17.18 Mb. It was very close to the reported Qftsc3D, which was flanked by wPt-2313 and wPt-6965[38]. QGsc.sicau-5DS, close to the reported QTL linked to the SSR marker Xbarc130[13], around 3.61 Mb explained a phenotypic variation of 10.75%. The other four QTL located at a greater distance from previously identified GSC genes or QTL regions may be novel loci.

Another two QTL associated with WGC were identified. QWgc.sicau-5DL flanked by markers AX-111139947 and AX-108791420 was located between 555.91 Mb and 556.72 Mb on the long arm of chromosome 5D. This QTL was different from previously reported genes/QTL associated with WGC. Therefore, we consider QWgc.sicau-5DL as a potentially novel QTL. PVE of another QTL, QWgc.sicau-7DL, located on the long arm of the chromosome 7D around 619.11 Mb, ranged from 4.19% to 5.05%; it was close to QGlu7D linked to the SSR markers Xwmc634 and Xwmc273.2 [36].

In this study, mapping was done on the Chinese Spring reference RefSeq v1.0 (IWGSC). Positioning of the 12 QTL revealed that four of them were covered by already reported QTL, three were close to reported QTL, and five were identified as potentially novel QTL. So far, few QTL have been associated with GSC and therefore, novel ones associated with GSC have been identified in this study.

Candidate genes for potentially novel QTL

Collinearity analysis of the candidate genes of the potentially novel QTL predicted 21 putative candidate genes, which may be associated with the quality traits. Of them, 15 candidate genes were identified in the three QTL (QGsc.sicau-1BL, QGsc.sicau-1DS, QGsc.sicau-2DL.2) associated with GSC. Six candidate genes, in QWgc.sicau-5DL, were predicted to be associated with WGC.

Four candidate genes were speculated to exist in QGsc.sicau-1BL. One putative gene, TraesCS1B02G338600, aligned with Arabidopsis JAL3 (Jacalin-related lectin 3), a carbohydrate binding protein that may play a role in determining GSC. concentrationTraesCS1B02G339200 is orthologous to Arabidopsis TRAF1A (TNF receptor-associated factor homolog 1a), which associates with autophagosomes and takes part in the regulation of autophagy dynamics [61]. Young and Gallie [62] have reported programmed cell death (PCD) in the starchy endosperm cells of wheat and maize during the final stages of seed development. Based on this report, we presume that TraesCS1B02G339200 may influenceconcentration GSC via autophagy (type Ⅱ PCD) in the starchy endosperm cells of wheat. TraesCS1B02G339500 and TraesCS1B02G339700 were found homologous to PSBW (Photosystem II reaction center W protein), which is involved in photosynthesis and photosystem II stabilization [63] and influence GSC in wheat.

Another eight candidate genes were predicted for QGsc.sicau-1DS associated with starch concentration. TraesCS1D02G022300 has sequences homologous to rice glycosyltransferase BC10. Glycosyltransferase catalyzes the transfer of sugars during starch biosynthesis [64.65]. TraesCS1D02G023300 aligned with the Arabidopsis gene PP2A1 (Phloem protein 2-like A1), which plays a role in carbohydrate binding similar to JAL3[66]. TraesCS1D02G024100 is homologous to the gene EXGA (Probable glucan 1,3-beta-glucosidase A) of Emericella nidulans. Gene ontology annotation indicated its role in polysaccharide catabolism. TraesCS1D02G024200, orthologous to the beta-galactosidase 1 Os01g0533400 in rice, might be involved in carbohydrate metabolism and in turn influence GSC. TraesCS1D02G026200 has sequences homologous to the rice gene WNK2, a probable cytoplasmic serine/threonine kinase involved in protein phosphorylation [67]. Grimaud [68] reported the significance of protein phosphorylation in starch synthesis. Meanwhile, TraesCS1D02G026500 and TraesCS1D02G032500 aligned with Arabidopsis cysteine protease XCP1 related to PCD [69]. Similar to TraesCS1B02G339200, TraesCS1D02G026500 and TraesCS1D02G032500 may also influence GSC of wheat via PCD. TraesCS1D02G031700 is orthologous to the Arabidopsis gene PGRL1A (PGR5-like protein 1A), which is involved in photosynthesis and photosynthetic electron transport in photosystem I [70，71].

We identified three candidate genes in QGsc.sicau-2DL.2. The orthologous gene of TraesCS2D02G277300 is Arabidopsis EMB1674 involved in abscisic acid (ABA)-activated signaling pathway. The balance between ABA and ethylene is crucial in PCD regulation in the starchy endosperm [72]. TraesCS2D02G278100 showed homology with CHIT5B (Class V chitinase) of Medicago truncatula. Functional analysis revealed its role in the polysaccharide catabolism similar to EXGA. TraesCS2D02G278200 aligned with the Arabidopsis gene TIF3H1 (Translation initiation factor 3 subunit H1), which responds to ABA, glucose, and sucrose levels [72]. ABA can regulate PCD and also assist in the conversion of glucose and sucrose into starch.

Six candidate genes were identified in QWgc.sicau-5DL. TraesCS5D02G546400 is orthologous to the Arabidopsis gene ERECTA (LRR receptor-like serine/threonine kinase) that regulates plant organ morphogenesis [73]. We speculate that grain morphogenesis in wheat is related to starch concentration in the grain. TraesCS5D02G549900 is homologous to the gene STP7 (Sugar transport protein 7) in Arabidopsis. It’s involved in cell wall sugar recycling [74]. TraesCS5D02G550500 aligned with the Arabidopsis gene ASIL2, which is the trihelix transcription factor that represses seed maturation program during early embryogenesis [75]. Seed maturation program involves starch and protein accumulation. Therefore, repression of the seed maturation program affects starch accumulation. The homologous gene of TraesCS5D02G551800 is Arabidopsis RCD1 (Radical-induced cell death 1), which is also involved in PCD [76]. TraesCS5D02G551900 and TraesCS5D02G552000 are orthologous to rice CIN4 (Beta-fructofuranosidase, insoluble isoenzyme 4) and 1-FEHw3 (Fructan 1-exohydrolase w3), respectively. These genes similar to Os01g0533400 take part in carbohydrate metabolism and might influence GSC of wheat.

We have shown that genome-wide association mapping effectively detected both stable and environment-specific QTL for grain protein concentration, grain starch concentration, and wet gluten concentration. Multi-trait chromosome regions have been detected and particularly the region on chromosome 2DL associated with grain protein concentration may be useful in MAS following proper validation. In the context of nutritional quality, 5 QTL regions were potentially novel control grain starch concentration or wet gluten concentration implying the possibility of using vegetation indices for indirect assessment for certain nutritional quality traits. Although some objective limitations for position comparison existed, we tried to improve the analysis threshold and compared with multiple linked markers to obtain maximum information. Furthermore, we will verify the potentially novel QTL using allelism test and fine mapping.

Plant materials

A total of 236 Sichuan wheat accessions including 76 landraces and 160 cultivars were used in the study (Additional file Table 1). All these accessions were homozygous lines were provided by Triticeae Research Institute, Sichuan Agricultural University (germplasm abbreviated as AS) and the Chinese Crop Germplasm Resources Bank (germplasm abbreviated as ZM) of China. The cultivars had different genetic background and were released from 1997 to 2016 from different breeding organizations, such as Sichuan Academy of Agricultural Sciences, Mianyang Academy of Agricultural Sciences, Neijiang Academy of Agricultural Sciences, Chengdu Institute of Biology, Chinese Academy of Sciences, Sichuan Agricultural University, and Southwest University of Science Technology of China.

Field trials

The accessions were planted in four different fields across two locations in Sichuan Province, China, under stripe rust stress: Chongzhou (30°33′N 103°39′E) and Mianyang (31°23′N 104°49′E). Field trials were conducted according to randomized block design with three replications in Chongzhou over three growing seasons (2017, CZ17; 2018, CZ18; and 2019, CZ19) and in Mianyang during one season (2017, MY17). In all field trials, 20 seeds of each accession were planted 10 cm apart in a 2 m row, with 30 cm between rows; three replicates were maintained per accession.

Measurement and analysis of quality traits

GPC and GSC in the whole-grain was determined by Near Infrared Reflectance Spectroscopy (NIR, Foss, Sweden) (AACC method 39-25). The gluten extraction was carried out adopting the procedure as described in AACC (2005). All these traits were expressed on a grain dry weight basis. The three quality traits were adjusted to 14% moisture concentration for further analysis [77]. Stripe rust infection type (IT) was evaluated at the adult stage following the method by Ye et al. [78].

Best linear unbiased prediction (BLUP) values for each accession across different locations were calculated by fitting the linear model in R package ‘lme4’ to eliminate the environmental impact on the quality traits. The genotypic and environmental variances (VG and VE) were also computed using ‘lme4’ package [79]. Broad-sense heritability (H²) for each of the quality traits was calculated across all test environments using the following formula: H2 = VG/(VG+VE) [80]. The phenotypic diversity was confirmed using Shannon-Weaver diversity index (H’) [81] calculated based on the BLUP values for each trait. SPSS 20.0 software (IBM Corp., Armonk, NY, USA) was used to compute Pearson’s correlation coefficient among four environments or three traits and to perform t-test to determine the significant differences in GPC, GSC and WSC between landraces and cultivars. Differences and interactions among wheat accessions, years and plant locations effects on variance were tested by two-way ANOVA.

Genotyping and molecular analysis

Genomic DNA was extracted using plant genomic DNA kit (Biofit Co., China) from a mix of leaves collected from five one-week-old seedlings of each accession. All 236 DNA samples were genotyped using 55K single nucleotide polymorphism (SNP) array (Affymetrix Axiom Wheat55K) at China Golden Marker Biotechnology Company Ltd. (Beijing, China). The SNP markers with missing values ≤ 10% and minor allele frequency (MAF) ≥ 5% were selected for further analysis. The polymorphic information concentration (PIC) 144,326 SNP marker was analyzed to evaluate the genetic diversity using software POWERMARKER v3.25[82].

Population structure, neighbor-joining (NJ) phylogeny, and linkage disequilibrium (LD) decay

The population structure (Q-matrix) of the wheat accessions were analyzed based on Bayesian model using STRUCTURE software (v2.3.4) [83]. Five independent runs for K (from 1 to 10) were performed using the admixture model with 10,000 burn-in and 10,000 Markov chain Monte Carlo (MCMC) iterations. The results from STRUCUTRE were summarized to obtain optimum population structure (optimum K) using delta K (ΔK) method in the web-based software STRUCTURE HARVESTER [84]. To understand the genetic relationship among all accessions, a phylogenetic tree was constructed using neighbor-joining (NJ) method based on shared allele distance [85] using TASSEL software v5.2.38[86]. The NJ phylogenetic tree was collapsed and formatted using iTOL v4[87]. The pairwise measure of linkage disequilibrium (LD) was estimated as squared allele frequency correlation (r2) between pairs of intra-chromosomal markers with known chromosomal position using TASSEL v5.2.38[64]. Significant pairwise markers were chosen with the threshold pDiseq < 0.001 and r2 > 0.1, and the LD decay plot and half decay distance were generated with r2 using the ggplot2 package in the R program [86].

Genome-wide association study (GWAS)

To identify the loci associated with GPC, GSC, and WGC, GWAS was performed on the 236 wheat accessions with the 44,326 effective markers using TASSEL v5.2.38 [86] based on the mixed linear model (MLM) with Q and K as covariates. To identify significantly associated loci, we set a threshold P value (−log10(P) ≥ 2.5) and the criterion to be detected in at least two test environments. Manhattan plots with P values were generated using ggplot2 package in R program to visualize the loci associated with the quality traits [88]. All the associated loci (−log10(P) ≥ 2.5) in the half decay distance region on the same chromosome were assigned to the same QTL block.

Candidate genes analysis

We further analyzed the putative candidate genes to identify the novel QTL. The genes included in the potentially novel QTL were selected based on the LD decay distance using the Chinese Spring reference genome (IWGSC RefSeq v1.0, RefSeq Annotation v1.1) [89]. Collinearity analysis was performed using BLAST at the EnsemblPlants website (https://plants.ensembl.org/Multi/Tools/Blast?db=core) with default parameters to identify the candidate genes.

BLUP: Best linear unbiased prediction; CV: Coefficient of variation; CZ: Chongzhou; FTN: Fertile tiller number; GWAS: Genome-wide association study; H': Shannon-Weaver diversity index; H²: Broad-sense heritability; IT: Infection type; GPC, grain protein concentration; GSC, grain starch concentration; WGC, wet gluten concentration

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the National Key Research and Development Program of China (2017YFD0100900, 2016YFD0102000 and 2016YFD0100100), the International Science and Technology Cooperation and Exchanges Programs of Science and Technology Department of Sichuan Province (2019YFH0063), and the Key Research and Development Program of Sichuan Province (2018NZDZX0002). The funders had no role in the study design, collection, analysis and interpretation of data, or in the writing of the report or decision to submit the article for publication.

Authors' contributions

ZE P, XL, Y, GY C: Conceptualization, Methodology, Software; Y L, ZH L: Data curation, Writing- Original draft preparation; SF Dai, JR W, W L, QT, J, YM W, YL Z: Writing – original draft & editing, Formal analysis, Investigation, Funding acquisition, Methodology, Writing –review & editing. All authors have reviewed and approved the final manuscript.

Acknowledgements

The authors thank Prof. Lihui Li and Xiuquan Li (Chinese Academy of Agricultural Sciences) for plant materials. We would like to thank TopEdit (www.topeditsci.com) for its linguistic assistance during the preparation of this manuscript.

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Table 1 The Pearson correlation coefficient among quality traits and infection type (IT)

Phenotype	GPC	GSC	WGC	IT
GPC	-	-	-	-
GSC	-0.412^**	-	-	-
WGC	0.801^**	-0.526^**	-	-
IT	-0.227^**	0.173^**	-0.156^*	-

GPC, grain protein content; GSC, grain starch content; WGC, wet gluten content;

^*, significant at p < 0.05; ^**, significant at p < 0.01

Table 2 The statistical analysis for quality traits, and the estimates of variance components and heritability

Parameter	GPC	GSC	WGC
Min	12.02	51.24	22.08
Max	14.13	59.89	26.67
Mean	12.90	55.87	24.10
σ²_G	0.3538^*	2.741^***	2.126^**
σ²_E	0.4743^***	2.136^***	3.476^***
σ²_GE	0.331^*	1.520^ns	1.993^**
σ²_e	0.355^*	1.592^ns	1.825^**
H²	0.644	0.841	0.656

GPC, grain protein content; GSC, grain starch content; WGC, wet gluten content; σ²_G, genotype variance estimate; σ²_E, environment variance estimate; σ²_GE, genotype × environment variance estimate; σ²_e, residual variance estimate; H², broad sense heritability; ^*, significant at p < 0.05; ^**, significant at p < 0.01; ^***, significant at p < 0.001; ^ns, not significant

Table 3 The analysis of phenotypic variations for landraces and cultivars based on BLUP values

Statistics	GPC^*			GSC^**			WGC^**
	Landrace	Cultivar	Landrace		Cultivar	Landrace		Cultivar
Max	14.13	13.85	57.69		59.89	26.67		25.57
Min	12.34	12.02	51.24		53.68	22.98		22.08
Mean	13.18	12.76	54.71		56.43	25.08		23.64
STDEV	0.39	0.37	1.13		1.24	0.80		0.77
CV	0.03	0.03	0.02		0.02	0.03		0.03
H’	0.82	0.77	0.77		0.83	0.82		0.60

BLUP, best linear unbiased prediction; GPC, grain protein content; GSC, grain starch content; WGC, wet gluten content; STDEV, standard deviation; CV, coefficient of variation; H’, the Shannon-Weaver diversity index; ^*and^** indicate the differences between landraces and cultivars are significant at the level p < 0.05 and p < 0.01, respectively

Table 4 The analysis of SNP markers and the genetic diversity

Chromosome	Markers	Length (Mb)	Marker/Mb	PIC
chr1A	2202	594	3.7	0.307
chr2A	2548	780	3.3	0.316
chr3A	2006	750	2.7	0.304
chr4A	2398	744	3.2	0.247
chr5A	2292	709	3.2	0.317
chr6A	2467	617	4.0	0.275
chr7A	2417	736	3.3	0.314
Subgenome A	16330	4930	3.3	0.297
chr1B	2424	689	3.5	0.327
chr2B	2377	801	3.0	0.303
chr3B	2421	830	2.9	0.305
chr4B	2418	673	3.6	0.291
chr5B	2436	713	3.4	0.330
chr6B	2447	720	3.4	0.313
chr7B	2308	750	3.1	0.318
Subgenome B	16831	5176	3.3	0.312
chr1D	1811	495	3.7	0.320
chr2D	1919	651	2.9	0.283
chr3D	1546	615	2.5	0.298
chr4D	832	509	1.6	0.270
chr5D	1537	565	2.7	0.307
chr6D	1542	473	3.3	0.282
chr7D	1978	638	3.1	0.303
Subgenome D	11165	3946	2.8	0.297
Mean	2111	669	3.2	0.303
Total	44326	14052	-	-

PIC, polymorphism information content index

Table 5 The details of QTLs associated with quality traits

Traits	QTL	SNP marker	Chro.	Position	SNP^a	Marker R² (%)	-log₁₀(P)	Environments	Reference
GPC	QGpc.sicau-1BS	AX-94755434	1B	51988962	G/A	5.7-6.2	2.6-2.9	MY17, CZ19	Echeverry-Solarte et al., 2015
	QGpc.sicau-2AS	AX-110402581	2A	1289378	G/A	5.9-8.1	2.8-3.2	MY17, CZ18	Bogard et al., 2013
	QGpc.sicau-2BS	AX-110435861	2B	25428600	A/G	5.3-6.7	2.5-3.3	MY17, CZ17	McCartney et al., 2006
GSC	QGsc.sicau-1BL	AX-110965712	1B	565220453	T/C	5.6-6.5	2.7-3.1	CZ17, CZ19	*
	QGsc.sicau-1DS	AX-109739785	1D	10940719	A/G	5.9-6.9	2.8	CZ18, CZ19	*
	QGsc.sicau-2DL.1	AX-110868690	2D	341265706	G/A	5.2-5.7	2.6	MY17, CZ19	*
	QGsc.sicau-2DL.2	AX-110185454	2D	348621484	C/T	6.3-8.8	2.8-4.1	MY17, CZ19	*
	QGsc.sicau-3BS	AX-110012661	3B	146183408	A/G	5.5-6.0	2.7-2.8	MY17, CZ19	Tian et al., 2015
	QGsc.sicau-3DS	AX-110348822	3D	17182951	T/C	5.1-6.6	2.5-2.7	MY17, CZ18	Deng et al., 2018
	QGsc.sicau-5DS	AX-86170796	5D	3609894	C/T	6.1-10.7	3.0-4.3	MY17, CZ18	Krystkowiak et al., 2017
WGC	QWgc.sicau-5DL	AX-111139947	5D	555906673	A/G	5.0-5.5	2.5	CZ17, CZ19	*
		AX-111454520	5D	556459064	T/G	6.1-6.2	2.8-3.1	CZ17, CZ19
		AX-108770574	5D	556472006	A/G	5.6-5.7	2.5-2.7	CZ17, CZ19
		AX-108791420	5D	556722226	T/C	7.6-7.9	3.7-3.8	CZ17, CZ19
	QWgc.sicau-7DL	AX-111186135	7D	619113235	G/A	4.2-5.0	2.5-2.6	CZ18, CZ19	Tian et al., 2015

GPC：grain protein concentration; GSC: grain starch concentration; WGC: wet gluten concentration

^a, the SNP with bold type indicates favourable allele; CZ17 = Chongzhou 2017; CZ18 = Chongzhou 2018; CZ19 = Chongzhou 2019; MY17 = Mianyang 2017; *, the potentially novel QTL

Addtiontional1.xlsx
The 236 Sichuan wheat accessions used in this study
Additonal2.pptx
The Pearson correlation coefficient among multiple environments for each trait
Additonal3.pptx
The putative candidate genes of potentially novel QTLs

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Genome-Wide association study identifies new elements on the genetic basis of quality-related traits in wheat across multiple environments

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